{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2.3 Layer-wise Relevance Propagation Part 2."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tensorflow Walkthrough"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Import Dependencies\n",
    "\n",
    "I made a custom `LRP` class for Layer-wise Relevance Propagation. If you are interested in the details, check out `models_3_1.py` in the models directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "\n",
    "from models.models_2_3 import MNIST_NN, MNIST_DNN, LRP\n",
    "from utils import pixel_range\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True)\n",
    "images = mnist.train.images\n",
    "labels = mnist.train.labels\n",
    "\n",
    "logdir = './tf_logs/2_3_LRP/'\n",
    "ckptdir = logdir + 'model'\n",
    "\n",
    "if not os.path.exists(logdir):\n",
    "    os.mkdir(logdir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Building Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with tf.name_scope('Classifier'):\n",
    "\n",
    "    # Initialize neural network\n",
    "    DNN = MNIST_DNN('DNN')\n",
    "\n",
    "    # Setup training process\n",
    "    X = tf.placeholder(tf.float32, [None, 784], name='X')\n",
    "    Y = tf.placeholder(tf.float32, [None, 10], name='Y')\n",
    "\n",
    "    activations, logits = DNN(X)\n",
    "    \n",
    "    tf.add_to_collection('LRP', X)\n",
    "    \n",
    "    for activation in activations:\n",
    "        tf.add_to_collection('LRP', activation)\n",
    "\n",
    "    cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=Y))\n",
    "\n",
    "    optimizer = tf.train.AdamOptimizer().minimize(cost, var_list=DNN.vars)\n",
    "\n",
    "    correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(Y, 1))\n",
    "    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
    "\n",
    "cost_summary = tf.summary.scalar('Cost', cost)\n",
    "accuray_summary = tf.summary.scalar('Accuracy', accuracy)\n",
    "summary = tf.summary.merge_all()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Training Network\n",
    "\n",
    "This is the step where the DNN is trained to classify the 10 digits of the MNIST images. Summaries are written into the logdir and you can visualize the statistics using tensorboard by typing this command: `tensorboard --lodir=./tf_logs`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0001 cost = 0.222209546 accuracy = 0.932272729\n",
      "Epoch: 0002 cost = 0.094443713 accuracy = 0.971490917\n",
      "Epoch: 0003 cost = 0.066750223 accuracy = 0.979527283\n",
      "Epoch: 0004 cost = 0.050566955 accuracy = 0.984109102\n",
      "Epoch: 0005 cost = 0.039164860 accuracy = 0.988254555\n",
      "Epoch: 0006 cost = 0.035111508 accuracy = 0.989545463\n",
      "Epoch: 0007 cost = 0.033497745 accuracy = 0.989563645\n",
      "Epoch: 0008 cost = 0.026709843 accuracy = 0.991909098\n",
      "Epoch: 0009 cost = 0.023360011 accuracy = 0.992781824\n",
      "Epoch: 0010 cost = 0.021341650 accuracy = 0.993490915\n",
      "Epoch: 0011 cost = 0.019153027 accuracy = 0.994236369\n",
      "Epoch: 0012 cost = 0.017887942 accuracy = 0.995036368\n",
      "Epoch: 0013 cost = 0.019383043 accuracy = 0.994763641\n",
      "Epoch: 0014 cost = 0.015831556 accuracy = 0.995163641\n",
      "Epoch: 0015 cost = 0.017971029 accuracy = 0.994836368\n",
      "Accuracy: 0.9793\n"
     ]
    }
   ],
   "source": [
    "sess = tf.InteractiveSession()\n",
    "sess.run(tf.global_variables_initializer())\n",
    "\n",
    "saver = tf.train.Saver()\n",
    "file_writer = tf.summary.FileWriter(logdir, tf.get_default_graph())\n",
    "\n",
    "# Hyper parameters\n",
    "training_epochs = 15\n",
    "batch_size = 100\n",
    "\n",
    "for epoch in range(training_epochs):\n",
    "    total_batch = int(mnist.train.num_examples / batch_size)\n",
    "    avg_cost = 0\n",
    "    avg_acc = 0\n",
    "    \n",
    "    for i in range(total_batch):\n",
    "        batch_xs, batch_ys = mnist.train.next_batch(batch_size)\n",
    "        _, c, a, summary_str = sess.run([optimizer, cost, accuracy, summary], feed_dict={X: batch_xs, Y: batch_ys})\n",
    "        avg_cost += c / total_batch\n",
    "        avg_acc += a / total_batch\n",
    "        \n",
    "        file_writer.add_summary(summary_str, epoch * total_batch + i)\n",
    "\n",
    "    print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.9f}'.format(avg_cost), 'accuracy =', '{:.9f}'.format(avg_acc))\n",
    "    \n",
    "    saver.save(sess, ckptdir)\n",
    "\n",
    "print('Accuracy:', sess.run(accuracy, feed_dict={X: mnist.test.images, Y: mnist.test.labels}))\n",
    "\n",
    "sess.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Restoring Subgraph\n",
    "\n",
    "Here we first rebuild the DNN graph from metagraph, restore DNN parameters from the checkpoint and then gather the necessary weights and biases for LRP using the `tf.get_collection()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./tf_logs/3_1_LRP/model\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "\n",
    "sess = tf.InteractiveSession()\n",
    "\n",
    "new_saver = tf.train.import_meta_graph(ckptdir + '.meta')\n",
    "new_saver.restore(sess, tf.train.latest_checkpoint(logdir))\n",
    "\n",
    "weights = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='.*kernel.*')\n",
    "biases = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='.*bias.*')\n",
    "activations = tf.get_collection('LRP')\n",
    "X = activations[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Attaching Subgraph for Calculating Relevance Scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conv_ksize = [1, 3, 3, 1]\n",
    "pool_ksize = [1, 2, 2, 1]\n",
    "conv_strides = [1, 1, 1, 1]\n",
    "pool_strides = [1, 2, 2, 1]\n",
    "\n",
    "weights.reverse()\n",
    "biases.reverse()\n",
    "activations.reverse()\n",
    "\n",
    "# LRP-alpha1-beta0\n",
    "lrp10 = LRP(1, activations, weights, biases, conv_ksize, pool_ksize, conv_strides, pool_strides, 'LRP10')\n",
    "\n",
    "# LRP-alpha2-beta1\n",
    "lrp21 = LRP(2, activations, weights, biases, conv_ksize, pool_ksize, conv_strides, pool_strides, 'LRP21')\n",
    "\n",
    "Rs10 = [lrp10(i) for i in range(10)]\n",
    "Rs21 = [lrp21(i) for i in range(10)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Calculating Relevance Scores $R(x_i)$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sample_imgs = []\n",
    "for i in range(10):\n",
    "    sample_imgs.append(images[np.argmax(labels, axis=1) == i][3])\n",
    "\n",
    "imgs10 = []\n",
    "imgs21 = []\n",
    "for i in range(10):\n",
    "    imgs10.append(sess.run(Rs10[i], feed_dict={X: sample_imgs[i][None,:]}))\n",
    "    imgs21.append(sess.run(Rs21[i], feed_dict={X: sample_imgs[i][None,:]}))\n",
    "\n",
    "sess.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7. Displaying Images for LRP-$\\alpha_1 \\beta_0$\n",
    "\n",
    "The relevance scores are visualized as heat maps. You can see which features/data points influenced the DNN most its decision making."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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GFXV31M2qYuon2Xs53VTpZyNi4udGSd+gNiTGHXUzM+u/zGZIy+cridkg6GzWl2emm5I0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\nzIzqVAjMKqZpvnSuNGtdlSrqnU7Uvwq8EZgPrAX+Nm/HiFgaEcMRMTx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9gqRRYLqkUUln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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1cae4afc978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,15))\n",
    "for i in range(5):\n",
    "    plt.subplot(5, 2, 2 * i + 1)\n",
    "    vmin, vmax = pixel_range(imgs10[2 * i])\n",
    "    plt.imshow(np.reshape(imgs10[2 * i], [28, 28]), vmin=-vmax, vmax=vmax, cmap='bwr')\n",
    "    plt.title('Digit: {}'.format(2 * i))\n",
    "    plt.colorbar()\n",
    "    \n",
    "    plt.subplot(5, 2, 2 * i + 2)\n",
    "    vmin, vmax = pixel_range(imgs10[2 * i + 1])\n",
    "    plt.imshow(np.reshape(imgs10[2 * i + 1], [28, 28]), vmin=-vmax, vmax=vmax, cmap='bwr')\n",
    "    plt.title('Digit: {}'.format(2 * i + 1))\n",
    "    plt.colorbar()\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8. Displaying Images for LRP-$\\alpha_2 \\beta_1$\n",
    "\n",
    "You can see that for LRP-$\\alpha_2 \\beta_1$, there are also spots with negative relevance scores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uuybDezyWXq1lYCC9Wsu0bRuS8ce3zU6fF5g8b79kvNG3zlkN4nPn+K7zTsip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\n/OiBuVlF9NdpNTMzs+1yyo8emJtVSC4zA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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1cae501d198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,15))\n",
    "for i in range(5):\n",
    "    plt.subplot(5, 2, 2 * i + 1)\n",
    "    vmin, vmax = pixel_range(imgs21[2 * i])\n",
    "    plt.imshow(np.reshape(imgs21[2 * i], [28, 28]), vmin=vmin, vmax=vmax, cmap='bwr')\n",
    "    plt.title('Digit: {}'.format(2 * i))\n",
    "    plt.colorbar()\n",
    "    \n",
    "    plt.subplot(5, 2, 2 * i + 2)\n",
    "    vmin, vmax = pixel_range(imgs21[2 * i + 1])\n",
    "    plt.imshow(np.reshape(imgs21[2 * i + 1], [28, 28]), vmin=vmin, vmax=vmax, cmap='bwr')\n",
    "    plt.title('Digit: {}'.format(2 * i + 1))\n",
    "    plt.colorbar()\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autoclose": false,
   "autocomplete": true,
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
   },
   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
